Watch Less, Feel More: Sim-to-Real RL for Generalizable Articulated Object Manipulation via Motion Adaptation and Impedance Control
Tan-Dzung Do, Nandiraju Gireesh, Jilong Wang, He Wang

TL;DR
This paper introduces a novel RL-based approach for generalizable articulated object manipulation that achieves high success rates in real-world zero-shot transfer by combining motion adaptation, impedance control, and observation history.
Contribution
It presents a new RL pipeline with variable impedance control and motion adaptation that reduces reliance on vision and improves sim-to-real transfer for articulated objects.
Findings
Achieved 84% success rate in real-world manipulation of unseen objects.
Reduced sim-to-real gap by using observation history and impedance control.
Enabled multi-staged manipulation without heuristic motion planning.
Abstract
Articulated object manipulation poses a unique challenge compared to rigid object manipulation as the object itself represents a dynamic environment. In this work, we present a novel RL-based pipeline equipped with variable impedance control and motion adaptation leveraging observation history for generalizable articulated object manipulation, focusing on smooth and dexterous motion during zero-shot sim-to-real transfer. To mitigate the sim-to-real gap, our pipeline diminishes reliance on vision by not leveraging the vision data feature (RGBD/pointcloud) directly as policy input but rather extracting useful low-dimensional data first via off-the-shelf modules. Additionally, we experience less sim-to-real gap by inferring object motion and its intrinsic properties via observation history as well as utilizing impedance control both in the simulation and in the real world. Furthermore, we…
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Taxonomy
TopicsRobot Manipulation and Learning · Human Pose and Action Recognition · Hand Gesture Recognition Systems
